The presence and complexity of political Twitter bots has increased in recent years, making it a very difficult task to recognize these accounts from real, human users. We intended to provide an answer to the following question: are temporal patterns of activity qualitatively different in fake and human accounts? We collected a large sample of tweets during the post-electoral conflict in the US in 2020 and performed supervised and non-supervised statistical learning technique sto quantify the predictive power of time-series features for human-bot recognition. Our results show that there are no substantial differences, suggesting that political bots are nowadays very capable of mimicking human behaviour. This finding reveals the need for novel, more sophisticated bot-detection techniques.